Climate-smart Agriculture (CSA) refers to agriculture and food systems that increase production, build resilience and adaptive capacity of food system and reduce emissions where appropriate. CSA intends to sustainably increase production and improve the resilience of food systems under climate change. However, the evidence based on the linkages between climate, agriculture and nutrition (‘C-A-N’) is complex and limited. The premise underlying this project is that innovative methods and metrics used in computer science, decision science and ecology can increase the information available to understand the linkages among C-A-N. Principally SCAN is funded by UK AID through the Innovative Methods and Metrics for Agriculture and Nutrition Action (IMMANA) program. CCAFS has provided supplemental funding.
The innovation of SCAN’s research comes from addressing three interrelated themes:
- Data acquisition: What are the theoretical limits for conducting surveys using Andorid-based surveys, call centers, SMS or voice-based recording?
- Data integration: Can we develop probabilistic approaches to compile information from disparate sources for monitoring C-A-N metrics and outcomes?
- Data analysis: Do alternatives statistical techniques such as hypervolumes provide new means to interpret and visualize multidimensional C-A-N relationships?
Fieldwork activities took place in Baringo and Kitui counties in Kenya, with the assistance of ICRAF team 8 enumerators (4 in each county) have gone to 4 villages/sub-locations in each county to implement C-A-N android based surveys with local farmers. Those two counties have been selected as they are part of concerted data gathering effort by various ongoing research programs and projects, the sub-location has been chosen due to safety factors in those counties and to maximize spatial coverage.
The approach of involving ICT technologies in the SCAN data acquisition has proved time and cost efficiency, the volume of data with associated validation and quality-checking requirements, ethics, and the need for high engagement. SCAN is a pilot interdisciplinary research project embedded within large-scale development processes. The results will help determine the optimal infrastructure to build coherent and harmonized datasets and support monitoring and decision making around C-A-N and CSA.
Photo credit: Neil Palmer (CIAT)